Literature DB >> 24803169

Safety modeling of suburban arterials in Shanghai, China.

Xuesong Wang1, Yang Song2, Rongjie Yu2, Grant G Schultz3.   

Abstract

As urbanization accelerates in Shanghai, land continues to develop along suburban arterials which results in more access points along the roadways and more congested suburban arterials; all these changes have led to deterioration in traffic safety. In-depth safety analysis is needed to understand the relationship between roadway geometric design, access features, traffic characteristics, and safety. This study examined 161 road segments (each between two adjacent signalized intersections) of eight suburban arterials in Shanghai. Information on signal spacing, geometric design, access features, traffic characteristics, and surrounding area types were collected. The effect of these factors on total crash occurrence was investigated. To account for the hierarchical data structure, hierarchical Bayesian models were developed for total crashes. To identify diverse effects on different crash injury severity, the total crashes were separated into minor injury and severe injury crashes. Bivariate hierarchical Bayesian models were developed for minor injury and severe injury to account for the correlation among different severity levels. The modeling results show that the density of signal spacing along arterials has a significant influence on minor injury, severe injury, and total crash frequencies. The non-uniform signal spacing has a significant impact on the occurrence of minor injury crashes. At the segment-level, higher frequencies of minor injury, severe injury, and total crashes tend to occur for the segments with curves, those with a higher density of access points, those with a higher percentage of heavy vehicles, and those in inner suburban areas. This study is useful for applications such as related engineering safety improvements and making access management policy.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Bivariate Bayesian models; Hierarchical Bayesian model; Risk factor; Safety analysis; Suburban arterial

Mesh:

Year:  2014        PMID: 24803169     DOI: 10.1016/j.aap.2014.04.005

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

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Authors:  Helai Huang; Qingyi Yin; David C Schwebel; Li Li; Guoqing Hu
Journal:  PLoS One       Date:  2016-04-12       Impact factor: 3.240

2.  Evaluation of the factors influencing the housing safety awareness of residents in Shanghai.

Authors:  Jin Ban; Longzhu Chen
Journal:  PLoS One       Date:  2020-01-24       Impact factor: 3.240

  2 in total

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